Prediction of Candidate Drugs for Treating Pancreatic Cancer by Using a Combined Approach

Inferring Drug Set and Identifying the Mechanism of Drugs for PC3

Drug repurposing is a strategy for discovering new applications of existing drugs for use in various diseases. Despite the use of structured networks in drug research, it is still unclear how drugs interact with one another or with genes. Prostate adenocarcinoma is the second leading cause of cancer mortality in the United States, with an estimated incidence of 288,300 new cases and 34,700 deaths in 2023. In our study, we used integrative information from genes, pathways, and drugs for machine learning methods such as clustering, feature selection, and enrichment pathway analysis. We investigated how drugs affect drugs and how drugs affect genes in human pancreatic cancer cell lines that were derived from bone metastases of grade IV prostate cancer. Finally, we identified significant drug interactions within or between clusters, such as estradiol-rosiglitazone, estradiol-diclofenac, troglitazone-rosiglitazone, celecoxib-rofecoxib, celecoxib-diclofenac, and sodium phenylbutyrate-valproic acid.

Upcoming Revolutionary Paths in Preclinical Modeling of Pancreatic Adenocarcinoma

To date, PDAC remains the cancer having the worst prognosis with mortality rates constantly on the rise. Efficient cures are still absent, despite all attempts to understand the aggressive physiopathology underlying this disease. A major stumbling block is the outdated preclinical modeling strategies applied in assessing effectiveness of novel anticancer therapeutics. Current in vitro preclinical models have a low fidelity to mimic the exact architectural and functional complexity of PDAC tumor found in human set, due to the lack of major components such as immune system and tumor microenvironment with its associated chemical and mechanical signals. The existing PDAC preclinical platforms are still far from being reliable and trustworthy to guarantee the success of a drug in clinical trials. Therefore, there is an urgent demand to innovate novel in vitro preclinical models that mirrors with precision tumor-microenvironment interface, pressure of immune system, and molecular and morphological aspects of the PDAC normally experienced within the living organ. This review outlines the traditional preclinical models of PDAC namely 2D cell lines, genetically engineered mice, and xenografts, and describing the present famous approach of 3D organoids. We offer a detailed narration of the pros and cons of each model system. Finally, we suggest the incorporation of two off-center newly born techniques named 3D bio-printing and organs-on-chip and discuss the potentials of swine models and in silico tools, as powerful new tools able to transform PDAC preclinical modeling to a whole new level and open new gates in personalized medicine.

A pharmacogenomic analysis using L1000CDS2 identifies BX-795 as a potential anticancer drug for primary pancreatic ductal adenocarcinoma cells

Pancreatic cancer is one of the leading causes of cancer death, mainly due to the absence of early diagnostic tool and effective therapeutic agents. To identify an effective therapeutic agent for pancreatic ductal adenocarcinoma cells (PDAC), we used 10 Gene Expression Omnibus (GEO) data sets and L1000CDS² pharmacogenetic search tool and obtained chemical "perturvants" that were predicted to reverse the abnormal gene expression changes in PDAC. Among 20 initial candidates, we measured IC₅₀ for six compounds and identified BX-795, PDK1/TBK1 inhibitor, as a therapeutic candidate. We found that BX-795 inhibits primary PDAC cell proliferation more effectively than normal cells. Following molecular analysis revealed that BX-795 down-regulates mTOR-GSK3β pathway and trigger apoptosis. Moreover, we found that BX-795 suppresses primary PDAC cell migration via downregulation of Snail and Slug. Finally, efficacy test in patient-derived xenograft model of PDAC showed BX-795 can inhibit in vivo tumor growth as efficient as gemcitabine and a combination with trametinib further suppresses tumor growth. Collectively, these results demonstrate the BX-795 as an effective therapeutic candidate for PDAC treatment.

Pancreas Cancer Precision Treatment Using Avatar Mice from a Bioinformatics Perspective

Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related death among solid malignancies. Unfortunately, PDAC lethality has not substantially decreased over the past 20 years. This aggressiveness is related to the genomic complexity and heterogeneity of PDAC, but also to the absence of an effective screening for the detection of early-stage tumors and a lack of efficient therapeutic options. Therefore, there is an urgent need to improve the arsenal of anti-PDAC drugs for an effective treatment of these patients. Patient-derived xenograft (PDX) mouse models represent a promising strategy to personalize PDAC treatment, offering a bench testing of candidate treatments and helping to select empirical treatments in PDAC patients with no therapeutic targets. Moreover, bioinformatics-based approaches have the potential to offer systematic insights into PDAC etiology predicting putatively actionable tumor-specific genomic alterations, identifying novel biomarkers and generating disease-associated gene expression signatures. This review focuses on recent efforts to individualize PDAC treatments using PDX models. Additionally, we discuss the current understanding of the PDAC genomic landscape and the putative druggable targets derived from mutational studies. PDAC molecular subclassifications and gene expression profiling studies are reviewed as well. Finally, latest bioinformatics methodologies based on somatic variant detection and prioritization, in silico drug response prediction, and drug repositioning to improve the treatment of advanced PDAC tumors are also covered.